Word Importance Modeling to Enhance Captions Generated by Automatic Speech Recognition for Deaf and Hard of Hearing Users

People who are deaf or hard-of-hearing (DHH) benefit from sign-language interpreting or live-captioning (with a human transcriptionist), to access spoken information. However, such services are not legally required, affordable, nor available in many settings, e.g., impromptu small-group meetings in the workplace or online video content that has not been professionally captioned. As Automatic Speech Recognition (ASR) systems improve in accuracy and speed, it is natural to investigate the use of these systems to assist DHH users in a variety of tasks. But, ASR systems are still not perfect, especially in realistic conversational settings, leading to the issue of trust and acceptance of these systems from the DHH community. To overcome these challenges, our work focuses on: (1) building metrics for accurately evaluating the quality of automatic captioning systems, and (2) designing interventions for improving the usability of captions for DHH users. The first part of this dissertation describes our research on methods for identifying words that are important for understanding the meaning of a conversational turn within transcripts of spoken dialogue. Such knowledge about the relative importance of words in spoken messages can be used in evaluating ASR systems (in part 2 of this dissertation) or creating new applications for DHH users of captioned video (in part 3 of this dissertation). We found that models which consider both the acoustic properties of spoken words as well as text-based features (e.g., pre-trained word embeddings) are more effective at predicting the semantic importance of a word than models that utilize only one of these types of features. The second part of this dissertation describes studies to understand DHH users\u27 perception of the quality of ASR-generated captions; the goal of this work was to validate the design of automatic metrics for evaluating captions in real-time applications for these users. Such a metric could facilitate comparison of various ASR systems, for determining the suitability of specific ASR systems for supporting communication for DHH users. We designed experimental studies to elicit feedback on the quality of captions from DHH users, and we developed and evaluated automatic metrics for predicting the usability of automatically generated captions for these users. We found that metrics that consider the importance of each word in a text are more effective at predicting the usability of imperfect text captions than the traditional Word Error Rate (WER) metric. The final part of this dissertation describes research on importance-based highlighting of words in captions, as a way to enhance the usability of captions for DHH users. Similar to highlighting in static texts (e.g., textbooks or electronic documents), highlighting in captions involves changing the appearance of some texts in caption to enable readers to attend to the most important bits of information quickly. Despite the known benefits of highlighting in static texts, research on the usefulness of highlighting in captions for DHH users is largely unexplored. For this reason, we conducted experimental studies with DHH participants to understand the benefits of importance-based highlighting in captions, and their preference on different design configurations for highlighting in captions. We found that DHH users subjectively preferred highlighting in captions, and they reported higher readability and understandability scores and lower task-load scores when viewing videos with captions containing highlighting compared to the videos without highlighting. Further, in partial contrast to recommendations in prior research on highlighting in static texts (which had not been based on experimental studies with DHH users), we found that DHH participants preferred boldface, word-level, non-repeating highlighting in captions

Assisted Interaction for Improving Web Accessibility: An Approach Driven and Tested by Userswith Disabilities

148 p.Un porcentaje cada vez mayor de la población mundial depende de la Web para trabajar, socializar, opara informarse entre otras muchas actividades. Los beneficios de la Web son todavía más cruciales paralas personas con discapacidades ya que les permite realizar un sinfín de tareas que en el mundo físico lesestán restringidas debido distintas barreras de accesibilidad. A pesar de sus ventajas, la mayoría depáginas web suelen ignoran las necesidades especiales de las personas con discapacidad, e incluyen undiseño único para todos los usuarios. Existen diversos métodos para combatir este problema, como porejemplo los sistemas de ¿transcoding¿, que transforman automáticamente páginas web inaccesibles enaccesibles. Para mejorar la accesibilidad web a grupos específicos de personas, estos métodos requiereninformación sobre las técnicas de adaptación más adecuadas que deben aplicarse.En esta tesis se han realizado una serie de estudios sobre la idoneidad de diversas técnicas de adaptaciónpara mejorar la navegación web para dos grupos diferentes de personas con discapacidad: personas conmovilidad reducida en miembros superiores y personas con baja visión. Basado en revisionesbibliográficas y estudios observacionales, se han desarrollado diferentes adaptaciones de interfaces web ytécnicas alternativas de interacción, que posteriormente han sido evaluadas a lo largo de varios estudioscon usuarios con necesidades especiales. Mediante análisis cualitativos y cuantitativos del rendimiento yla satisfacción de los participantes, se han evaluado diversas adaptaciones de interfaz y métodosalternativos de interacción. Los resultados han demostrado que las técnicas probadas mejoran el acceso ala Web y que los beneficios varían según la tecnología asistiva usada para acceder al ordenador